This application is the US national phase of international application PCT/GB2004/001070 filed 12 Mar. 2004 which designated the U.S. and claims benefit of GB 0307406.9, dated 31 Mar. 2003, the entire content of which is hereby incorporated by reference.
1. Technical Field
The present invention relates to systems and methods for the analysis of data from a monitoring system for monitoring a dynamic system.
2. Related Art
Diagnosing abnormal behaviour of a system (e.g. a technical system, environmental conditions, vital signs, etc) is similar to a physician's diagnosis based on observing symptoms of a patient. Medical doctors are capable of interpreting symptoms and making a diagnosis based on data obtained from observing a patient. Even if the observed symptoms are not sufficient to determine the cause of an illness, medical doctors can often determine that a symptom or measurement is not normal because they know from experience what is normal in a patient and what is not.
In order to replicate such an intelligent diagnosis process in an automated Condition Monitoring System (CMS), the CMS must know “what is normal” and “what is not normal”. Known CMSs use signatures to achieve that goal. A signature is a limited amount of data that represents a certain feature of the environment that is monitored by a sensor. A signature, for example, can be as simple as a temperature value or as complex as the Fourier transform of a current observed over a certain time. A diagnosis based on signature analysis can be realised by the following steps:
1. Signature acquisition
2. Comparison of an incoming signature with reference signatures.
3. Deciding if the incoming signature is normal or abnormal.
4. Interpreting the signature in order to make a proper diagnosis.
CMSs usually comprise steps 1-3, while step 4 generally involves the intervention of a domain expert after an alarm has been raised by a CMS.
Most of the incoming signatures are usually tainted with noise, which makes their recognition difficult. Therefore, in addition to all available classified signatures (reference signatures) the signature database must also contain tolerance levels for each signature. Tolerance levels are required to avoid false alarms. The information from the signature database is used to classify sensor data into three different states. If the system recognises the incoming signature as a class member of its database it can be directly classified as either normal or abnormal, and raise an alarm if the signature is considered to be abnormal. If the system does not recognise the incoming signature to be within the tolerance level of any reference signature, then it will be considered as unknown and possibly abnormal. In this case the system will also raise an alarm. Based on the signature that caused an alarm to be raised, a domain expert will be able make a diagnosis and determine if the signature actually indicates an abnormal state, and if intervention is required.
Detecting Abnormal Conditions in Sensor Data
In order to detect abnormal states in any environment automatically, the use of sensors is required. There are many different types of sensors, which can have different degrees of reliability. For example, in a refinery the use of chemical sensors can detect gas leakage, whereas in a power station electrical sensors may be used to detect dangerously high voltage. Some common types of sensor include mechanical sensors, temperature sensors, magnetic and electro-magnetic field sensors, laser sensors, infrared sensors, ultraviolet sensors, radiation sensors and acoustic sensors.
Sensor data is in general observed in the time domain and the frequency domain. Amplitude, mean, range, interference noise and standard deviation are commonly used functions for analytical analysis of sensor data. They can be analysed individually or combined in a multi-dimensional approach.
When a sensor system is designed, several factors influence the choice of sensors, for example, linearity, resolution, spectral pass band, accuracy, response time, signal noise ratio, etc. All these factors have also to be taken into consideration for the specification of threshold and tolerance levels. FIGS. 1 to 4 illustrate an example of a condition monitoring process that comprises obtaining a signature from a sensor, pre-processing it and applying a transformation for finally classifying the signature.
Simple CMSs detect abnormal conditions by comparing the incoming sensor data against thresholds, which are usually statistically characteristic values like mean, standard deviation, minimum, maximum, etc (see FIG. 5).
A more complex detection mechanism compares incoming signatures against reference signatures. The comparison can be computed by different techniques depending on the complexity of the problem. A simple example is to subtract the incoming signature from a reference signature stored in a database. The difference between the two signals is called the error and the amplitude of this error will define if the two signals are close or not. The mean square error can also be computed for getting a better estimation of this error. FIG. 6 illustrates this approach.
A simple way of determining if an error signal indicates an abnormal condition is to use thresholds. A more complex evaluation of an error signal would be based on the computation of several characteristics of the error signal, which are than compared against threshold values. Threshold values for evaluating error signals have to be carefully chosen in order to maximise the precision of the detection. FIG. 7 illustrates how the levels of upper and lower thresholds may be chosen in order to allow an alarm to be triggered when a signal outside a “normal” range occurs. The precision will depend on the choice of these levels as well as on the quality of the sensors used and the quality of the data acquisition system.
Known condition monitoring systems use condition libraries against which sensor data is matched. For example, the article “HISS—A new approach to intelligent supervision” (Kai Michels, Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Vancouver, 25-28 July), IEEE Piscataway, 2001 (ISBN: 0-78037079-1), pp. 1110-1115) provides a solution for detecting leaks in gas pipelines by using audio sensors. The sound recorded by an audio sensor is matched against a library of sounds indicating leakage and sounds indicating normal environmental sounds. If a sound recorded by the sensor is closer to a sound indicating leakage than to a normal sound the monitoring software raises an alarm. Further, it is possible to use artificial intelligence technology to carry out pattern recognition and decide what conditions should raise alarms in order to monitor the state of a system. An example of this is disclosed in U.S. Pat. No. 6,327,550 (Vinberg et al), which relates to a method and apparatus for such state monitoring whereby a system is educated, during an initial “learning phase”, to identify recognisable “common modes” of a monitored system, which are clusters of commonly occurring states of that system. During a later “monitoring phase” the state monitoring system continuously monitors the system by comparing state vectors of the system with the recognised common modes previously identified by pattern recognition during the learning period, and raises an alarm whenever a state vector appears that does not lie within one of the recognised common modes. Also during the monitoring phase the system is able to update its degree of learning in the following manner. A human manager or automated management tool may study alarm messages, and even inspect the managed system, and if it is determined that an alarm message was raised in respect of a common situation that should be included among the common modes for future monitoring, the system is able to add the relevant new state to the existing set of common modes.
Prior art U.S. Pat. No. 5,890,142 relates to an apparatus for monitoring system condition, the apparatus including a predicting section which is said to generate a data vector whose parameter is said to be determined by timeseries data of the system and which is said to obtain a prediction value of the timeseries data of a predetermined time future by means of chaotic inference based on the behaviour of an attractor generated in a reconstruction space by an embedding operation of the data vector. The system appears then to make short-term predictions based on an assumption that the behaviour of the system is chaotic, and judge whether the observed system is in a normal or an abnormal condition.
It will be evident that condition monitoring in known systems can generally only be usefully applied if normal and abnormal conditions of the monitored system are known and can be specified. That means CMSs are not suitable for use in ill-defined domains or domains where abnormal conditions have not been observed before, are not likely to be observed and cannot be created or predicted easily. An example would be the failure of an expensive piece of machinery. It is very desirable to predict failure well in advance in order to schedule maintenance in time. Failure may be devastating such that it is not possible to drive the monitored machine into failure mode in order to record failure conditions. There may be only vague and uncertain knowledge about thresholds of monitored signals and this knowledge may not be sufficient to describe failure sufficiently. Another example is monitoring care-dependent patients in their homes. It is paramount that the necessity of intervention by care personnel is detected with high accuracy, e.g. if the person fails to get up at the usual time of day or has fallen down. However, it is also important that false alarms are avoided or the monitoring system would not be trusted anymore and may even be switched off. It is therefore important that the monitoring system adapts to the monitored patient and learns what normal behaviour for that person means. For both examples it is easy to see that a single sensor is generally not sufficient. Generally, but not exclusively, a multitude of sensors is required thus creating a multi-dimensional sensor data space. Information from individual sensors may be suitably combined in order to enable decisions to be made about abnormal situations. This will be referred to as “sensor data fusion”.
For the examples discussed above and for similar complex scenarios we face the problem of setting up a CMS properly because it is often difficult or impossible to define normal and abnormal conditions in a high-dimensional sensor data space. If an exhaustive number of examples of normal and abnormal situations were available, a supervised learning algorithm could be used to create a classifier set which could be used by the CMS. However, in scenarios such as those considered above, only examples of normal situations may be available and thus normal learning procedure cannot be used.
One of the main problems in deploying a sensor based CMS is to establish normal and abnormal conditions for a multitude of sensors monitored by the CMS. If abnormal conditions or failure states are not known or are ill-defined, a CMS requiring such information cannot be used. Embodiments of the present invention address the problems encountered when deploying a CMS under such conditions.